OpenRaiser/Intern-Atlas huggingface dataset:
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
Venue: arXiv cs.SD 2022
Year: 2022

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.
Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks
Venue: arXiv cs.GT 2022
Year: 2022

Federated learning (FL) represents a promising distributed machine learning paradigm that allows smart devices to collaboratively train a shared model via providing local data sets. However, problems considering multiple co-existing FL services and different types of service providers are rarely studied. In this paper, we investigate a multiple FL service trading problem in Unmanned Aerial Vehicle (UAV)-aided networks, where FL service demanders (FLSDs) aim to purchase various data sets from feasible clients (smart devices, e.g., smartphones, smart vehicles), and model aggregation services from UAVs, to fulfill their requirements. An auction-based trading market is established to facilitate the trading among three parties, i.e., FLSDs acting as buyers, distributed located client groups acting as data-sellers, and UAVs acting as UAV-sellers. The proposed auction is formalized as a 0-1 integer programming problem, aiming to maximize the overall buyers' revenue via investigating winner determination and payment rule design. Specifically, since two seller types (data-sellers and UAV-sellers) are considered, an interesting idea integrating seller pair and joint bid is introduced, which turns diverse sellers into virtual seller pairs. Vickrey-Clarke-Groves (VCG)-based, and one-sided matching-based mechanisms are proposed, respectively, where the former achieves the optimal solutions, which, however, is computationally intractable. While the latter can obtain suboptimal solutions that approach to the optimal ones, with low computational complexity, especially upon considering a large number of participants. Significant properties such as truthfulness and individual rationality are comprehensively analyzed for both mechanisms. Extensive experimental results verify the properties and demonstrate that our proposed mechanisms outperform representative methods significantly.
Pixel to Binary Embedding Towards Robustness for CNNs
Venue: arXiv cs.CV 2022
Year: 2022

There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to an input, and the performances of CNNs are degraded when the distribution of input is shifted by a transformation never seen during training (e.g., the blur effect). There are approaches to replace pixel values with binary embeddings to tackle the problem of adversarial perturbations, which successfully improve robustness. In this work, we propose Pixel to Binary Embedding (P2BE) to improve the robustness of CNNs. P2BE is a learnable binary embedding method as opposed to previous hand-coded binary embedding methods. P2BE outperforms other binary embedding methods in robustness against adversarial perturbations and visual corruptions that are not shown during training.
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs
Venue: arXiv cs.LG 2022
Year: 2022

Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. We use a local distance preserving loss that is based on the continuous k-nearest neighbours graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. We generalise this approach to hierarchical variational auto-encoders thus learning generative models with geometrically consistent latent and data spaces. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

Quantized neural networks have drawn a lot of attention as they reduce the space and computational complexity during the inference. Moreover, there has been folklore that quantization acts as an implicit regularizer and thus can improve the generalizability of neural networks, yet no existing work formalizes this interesting folklore. In this paper, we take the binary weights in a neural network as random variables under stochastic rounding, and study the distribution propagation over different layers in the neural network. We propose a quasi neural network to approximate the distribution propagation, which is a neural network with continuous parameters and smooth activation function. We derive the neural tangent kernel (NTK) for this quasi neural network, and show that the eigenvalue of NTK decays at approximately exponential rate, which is comparable to that of Gaussian kernel with randomized scale. This in turn indicates that the Reproducing Kernel Hilbert Space (RKHS) of a binary weight neural network covers a strict subset of functions compared with the one with real value weights. We use experiments to verify that the quasi neural network we proposed can well approximate binary weight neural network. Furthermore, binary weight neural network gives a lower generalization gap compared with real value weight neural network, which is similar to the difference between Gaussian kernel and Laplace kernel.
Faster Optimization-Based Meta-Learning Adaptation Phase
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks require a large amount of annotated data to learn. Meta-learning algorithms propose a way to decrease the number of training samples to only a few. One of the most prominent optimization-based meta-learning algorithms is Model-Agnostic Meta-Learning (MAML). However, the key procedure of adaptation to new tasks in MAML is quite slow. In this work we propose an improvement to MAML meta-learning algorithm. We introduce Lambda patterns by which we restrict which weight are updated in the network during the adaptation phase. This makes it possible to skip certain gradient computations. The fastest pattern is selected given an allowed quality degradation threshold parameter. In certain cases, quality improvement is possible by a careful pattern selection. The experiments conducted have shown that via Lambda adaptation pattern selection, it is possible to significantly improve the MAML method in the following areas: adaptation time has been decreased by a factor of 3 with minimal accuracy loss; accuracy for one-step adaptation has been substantially improved.
Intrinsically motivated option learning: a comparative study of recent methods
Venue: arXiv cs.AI 2022
Year: 2022

Options represent a framework for reasoning across multiple time scales in reinforcement learning (RL). With the recent active interest in the unsupervised learning paradigm in the RL research community, the option framework was adapted to utilize the concept of empowerment, which corresponds to the amount of influence the agent has on the environment and its ability to perceive this influence, and which can be optimized without any supervision provided by the environment's reward structure. Many recent papers modify this concept in various ways achieving commendable results. Through these various modifications, however, the initial context of empowerment is often lost. In this work we offer a comparative study of such papers through the lens of the original empowerment principle.
Constant-Round Linear-Broadcast Secure Computation with Penalties
Venue: arXiv cs.CR 2022
Year: 2022

It is known that Bitcoin enables achieving fairness in secure computation by imposing monetary penalties on adversarial parties. This functionality is called secure computation with penalties. Bentov and Kumaresan (Crypto 2014) introduced the claim-or-refund functionality that can be implemented via Bitcoin. They achieved secure computation with penalties with $O(n)$ rounds and $O(n)$ broadcasts for any function, where $n$ is the number of parties. After that, Kumaresan and Bentov (CCS 2014) showed a constant-round protocol. Unfortunately, this protocol requires $O(n^2)$ broadcasts. As far as we know, no protocol achieves $O(1)$ rounds and $O(n)$ broadcasts based on Bitcoin. This work accomplishes such efficiency in secure computation with penalties. We first show a protocol in a slightly relaxed setting called secure computation with non-equivalent penalties. This setting is the same as secure computation with penalties except that every honest party receives more than a predetermined amount of compensation, while the previous one requires that every honest party receives the same amount of compensation. Namely, our setting allows the compensations for honest parties to be non-equivalent. Moreover, we present a technique to remove the non-equivalence of our protocol without sacrificing efficiency. We then propose a new ideal functionality called claim-refund-or-give that can be implemented via Bitcoin.
SBvote: Scalable Self-Tallying Blockchain-Based Voting
Venue: arXiv cs.CR 2022
Year: 2022

Decentralized electronic voting solutions represent a promising advancement in electronic voting. One of the e-voting paradigms, the self-tallying scheme, offers strong protection of the voters' privacy while making the whole voting process verifiable. Decentralized smart contract platforms became interesting practical instantiation of the immutable bulletin board that this scheme requires to preserve its properties. Existing smart contract-based approaches employing the self-tallying scheme (such as OVN or BBB-Voting) are only suitable for a boardroom voting scenario due to their scalability limitation. The goal of our work is to build on existing solutions to achieve scalability without losing privacy guarantees and verifiability. We present SBvote, a blockchain-based self-tallying voting protocol that is scalable in the number of voters and therefore suitable for large-scale elections. The evaluation of our proof-of-concept implementation shows that the protocol's scalability is limited only by the underlying blockchain platform. We evaluated the scalability of SBvote on two public smart contract platforms -- Gnosis and Harmony. Despite the limitations imposed by the throughput of the blockchain platform, SBvote can accommodate elections with millions of voters.
Combining BMC and Fuzzing Techniques for Finding Software Vulnerabilities in Concurrent Programs
Venue: arXiv cs.SE 2022
Year: 2022

Finding software vulnerabilities in concurrent programs is a challenging task due to the size of the state-space exploration, as the number of interleavings grows exponentially with the number of program threads and statements. We propose and evaluate EBF (Ensembles of Bounded Model Checking with Fuzzing) -- a technique that combines Bounded Model Checking (BMC) and Gray-Box Fuzzing (GBF) to find software vulnerabilities in concurrent programs. Since there are no publicly-available GBF tools for concurrent code, we first propose OpenGBF -- a new open-source concurrency-aware gray-box fuzzer that explores different thread schedules by instrumenting the code under test with random delays. Then, we build an ensemble of a BMC tool and OpenGBF in the following way. On the one hand, when the BMC tool in the ensemble returns a counterexample, we use it as a seed for OpenGBF, thus increasing the likelihood of executing paths guarded by complex mathematical expressions. On the other hand, we aggregate the outcomes of the BMC and GBF tools in the ensemble using a decision matrix, thus improving the accuracy of EBF. We evaluate EBF against state-of-the-art pure BMC tools and show that it can generate up to 14.9% more correct verification witnesses than the corresponding BMC tools alone. Furthermore, we demonstrate the efficacy of OpenGBF, by showing that it can find 24.2% of the vulnerabilities in our evaluation suite, while non-concurrency-aware GBF tools can only find 0.55%. Finally, thanks to our concurrency-aware OpenGBF, EBF detects a data race in the open-source wolfMqtt library and reproduces known bugs in several other real-world programs, which demonstrates its effectiveness in finding vulnerabilities in real-world software.
Local Dependence and Guarding
Venue: arXiv cs.LO 2022
Year: 2022

We study LFD, a base logic of functional dependence introduced by Baltag and van Benthem (2021) and its connections with the guarded fragment GF of first-order logic. Like other logics of dependence, the semantics of LFD uses teams: sets of permissible variable assignments. What sets LFD apart is its ability to express local dependence between variables and local dependence of statements on variables. Known features of LFD include decidability, explicit axiomatization, finite model property, and a bisimulation characterization. Others, including the complexity of satisfiability, remained open so far. More generally, what has been lacking is a good understanding of what makes the LFD approach to dependence computationally well-behaved, and how it relates to other decidable logics. In particular, how do allowing variable dependencies and guarding quantifiers compare as logical devices? We provide a new compositional translation from GF into LFD, and conversely, we translate LFD into GF in an `almost compositional' manner. Using these two translations, we transfer known results about GF to LFD in a uniform manner, yielding, e.g., tight complexity bounds for LFD satisfiability, as well as Craig interpolation. Conversely, e.g., the finite model property of LFD transfers to GF. Thus, local dependence and guarding turn out to be intricately entangled notions.
Low-complexity deep learning frameworks for acoustic scene classification
Venue: arXiv cs.SD 2022
Year: 2022

In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC). The proposed frameworks can be separated into four main steps: Front-end spectrogram extraction, online data augmentation, back-end classification, and late fusion of predicted probabilities. In particular, we initially transform audio recordings into Mel, Gammatone, and CQT spectrograms. Next, data augmentation methods of Random Cropping, Specaugment, and Mixup are then applied to generate augmented spectrograms before being fed into deep learning based classifiers. Finally, to achieve the best performance, we fuse probabilities which obtained from three individual classifiers, which are independently-trained with three type of spectrograms. Our experiments conducted on DCASE 2022 Task 1 Development dataset have fullfiled the requirement of low-complexity and achieved the best classification accuracy of 60.1%, improving DCASE baseline by 17.2%.
Graph Comparison Based on Adjacency Function Matrix
Venue: arXiv cs.DM 2022
Year: 2022

In this paper, we present a new metric distance for comparing two large graphs to find similarities and differences between them based on one of the most important graph structural properties, which is Node Adjacency Information, for all vertices in the graph. Then, we defined a new function and some parameters to find the distance of two large graphs using different neighbors of vertices. There are some methods which they focused on the other features of graphs to obtain the distance between them, but some of them are Node Correspondence which means their node set have the same size. However, in this paper, we introduce a new method which can find the distance between two large graphs with different size of node set.
Dataset: Dependency Networks of Open Source Libraries Available Through CocoaPods, Carthage and Swift PM
Venue: arXiv cs.SE 2022
Year: 2022

Third party libraries are used to integrate existing solutions for common problems and help speed up development. The use of third party libraries, however, can carry risks, for example through vulnerabilities in these libraries. Studying the dependency networks of package managers lets us better understand and mitigate these risks. So far, the dependency networks of the three most important package managers of the Apple ecosystem, CocoaPods, Carthage and Swift PM, have not been studied. We analysed the dependencies for all publicly available open source libraries up to December 2021 and compiled a dataset containing the dependency networks of all three package managers. The dependency networks can be used to analyse how vulnerabilities are propagated through transitive dependencies. In order to ease the tracing of vulnerable libraries we also queried the NVD database and included publicly reported vulnerabilities for these libraries in the dataset.
Social Practices: a Complete Formalization
Venue: arXiv cs.AI 2022
Year: 2022

Multi-agent models are a suitable starting point to model complex social interactions. However, as the complexity of the systems increase, we argue that novel modeling approaches are needed that can deal with inter-dependencies at different levels of society, where many heterogeneous parties (software agents, robots, humans) are interacting and reacting to each other. In this paper, we present a formalization of a social framework for agents based on the concept of Social Practices as high level specifications of normal (expected) behavior in a given social context. We argue that social practices facilitate the practical reasoning of agents in standard social interactions. Thus they can support deliberations for complex situations just like conventions and norms. However, they also come with a social context that gives handles for social planning and deliberation in top of the normal functional deliberation. The main goal of this paper is to give a formalization of social practices that can be used as a basis for implementations and defining precise structures within which social learning can take place.
Vildehaye: A Family of Versatile, Widely-Applicable, and Field-Proven Lightweight Wildlife Tracking and Sensing Tags
Venue: arXiv cs.NI 2022
Year: 2022

We describe the design and implementation of Vildehaye, a family of versatile, widely-applicable, and field-proven tags for wildlife sensing and radio tracking. The family includes 6 distinct hardware designs for tags, 3 add-on boards, a programming adapter, and base stations; modular firmware for tags and base stations (both standalone low-power embedded base stations and base stations tethered to a computer running Linux or Windows); and desktop software for programming and configuring tags, monitoring tags, and downloading and processing sensor data. The tags are versatile: they support multiple packet formats, data rates, and frequency bands; they can be configured for minimum mass (down to less than 1g), making them applicable to a wide range of flying and terrestrial animals, or for inclusion of important sensors and large memories; they can transmit packets compatible with time-of-arrival transmitter-localization systems, tag identification and state packets, and they can reliably upload sensor data through their radio link. The system has been designed, upgraded, and maintained as an academic research project, but it has been extensively used by 5 different groups of ecologists in 4 countries over a period of 5 years. More than 7100 tags have been produced and most of these have been deployed. Production used 41 manufacturing runs. The tags have been used in studies that so far resulted in 9 scientific publications in ecology (including in Science). The paper describes innovative design aspects of Vildehaye, field-use experiences, and lessons from the design, implementation, and maintenance of the system. Both the hardware and software of the system are open.
Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
Venue: arXiv cs.CL 2022
Year: 2022

Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.
Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms
Venue: arXiv cs.AI 2022
Year: 2022

Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data. Symbolic regression addresses this issue by searching the space of all possible free form equations that can be constructed from elementary algebraic functions. While explicit mathematical functions can be rediscovered this way, the determination of unknown numerical constants during search has been an often neglected issue. We propose a new multi-objective memetic algorithm that exploits a differentiable Cartesian Genetic Programming encoding to learn constants during evolutionary loops. We show that this approach is competitive or outperforms machine learned black box regression models or hand-engineered fits for two applications from space: the Mars express thermal power estimation and the determination of the age of stars by gyrochronology.